Myocardial fibrosis underpins a number of cardiovascular conditions and is difficult to identify with standard histologic techniques. Challenges include imaging, defining an objective threshold for classifying fibrosis as mild or severe, as well as understanding the molecular basis for these changes.
To develop a novel, rapid, label-free approach to accurately measure and quantify the extent of fibrosis in cardiac tissue using infrared spectroscopic imaging.
We performed infrared spectroscopic imaging and combined that with advanced machine learning–based algorithms to assess fibrosis in 15 samples from patients belonging to the following 3 classes: (1) nonpathologic (control) donor hearts; (2) patients receiving transplant; and (3) tissue from patients undergoing implantation of ventricular assist device.
Our results show excellent sensitivity and accuracy for detecting myocardial fibrosis as demonstrated by high area under the curve of 0.998 in the receiver-operating characteristic curve measured from infrared imaging. Fibrosis of various morphologic subtypes are then demonstrated with virtually generated picrosirius red images, which show good visual and quantitative agreement (correlation coefficient = 0.92, ρ = 7.76 × 10−15) with stained images of the same sections. Underlying molecular composition of the different subtypes were investigated with infrared spectra showing reproducible differences presumably arising from differences in collagen subtypes and/or crosslinking.
Infrared imaging can be a powerful tool in studying myocardial fibrosis and gleaning insights into the underlying chemical changes that accompany it. Emerging methods suggest that the proposed approach is compatible with conventional optical microscopy and its consistency makes it translatable to the clinical setting for real-time diagnoses as well as for objective and quantitative research.
The authors have no relevant financial interest in the products or companies described in this article.
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Zimmermann and Mukherjee contributed equally to this work.